M. Zribi

Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data

This paper presents an original methodology to retrieve surface (<5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellites. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. As a result, a mixed target model is proposed. The WSC backscattered signal may be represented as a combination of the vegetation and bare soil contributions weighted by their respective fractional covers. Over our temperate regions and time periods of interest, the vegetation signal is assumed to be principally due to forests backscattered signal. Then, thanks to the high spatial resolution of the SAR instrument, the forest contribution may be quantified from the analysis of the SAR image, and then removed from the total WSC signal in order to estimate the soil contribution. Finally, the Integral Equation Model (IEM, [IEEE Transactions on Geoscience and Remote Sensing, 30 (2), (1992) 356]) is used to estimate the effect of surface roughness and to retrieve surface soil moisture from the WSC multi-angular measurements. This methodology has been developed and applied on ERS data acquired over three different Seine river watersheds in France, and for a 3-year time period. The soil moisture estimations are compared with in situ ground measurements. High correlations (R 2 greater than 0.8) are observed for the three study watersheds with a root mean square

Soil moisture mapping based on ASAR/ENVISAT radar data over a Sahelian region

The analysis of feedbacks between continental surfaces and the atmosphere is one of the key factors to understanding African Monsoon dynamics. For this reason, the monitoring of surface parameters, in particular soil moisture, is very important. Satellite remote sensing appears to be the most suitable means of obtaining data relevant to such parameters. The present paper presents a methodology applied to the mapping and monitoring of surface soil moisture over the Kori Dantiandou region in Niger, using data provided by the ASAR/ENVISAT radar instrument. The study is based on 15 sets of ASAR/ENVISAT C‐band radar data, acquired during the 2004 and 2005 rainy seasons. Simultaneously with radar acquisitions, ground soil moisture measurements were carried out in a large number of test fields. Soil moisture was estimated only for fields with bare soil or low‐density vegetation, using low‐incidence‐angle radar data (IS1 configuration). A mask was developed, using SPOT/HRV data and DTM, for use over areas characterized by high‐density vegetation cover, pools, and areas with high slopes. Soil moisture estimations are based on horizontal‐ and vertical‐polarization radar data. In order to double the temporal frequency of soil moisture estimations, IS2 data were used with IS1 data, with all data normalized to a single incidence angle. A high correlation is observed between in situ measurements and processed radar data. An empirical inversion technique is proposed, to estimate surface soil moisture from dual‐polarization data with a spatial resolution of approximately 1 km. Surface soil moisture maps are presented for all the studied sites, at various dates in 2004 and 2005. Of particular interest, these maps reveal convective precipitation scales associated with strong spatial variations in surface soil moisture.

Relationship between soil moisture and vegetation in the Kairouan plain region of Tunisia using low spatial resolution satellite data

[1]xa0The present paper proposes an empirical approach for the modeling of vegetation development, using moisture measurements only. The study is based simply on the use of two databases: one containing soil moisture products derived from ERS scatterometer data over the period 1991–2006 and the other containing normalized difference vegetation indices (NDVI) derived from advanced very high resolution radiometer over the period 1991–2000. The study is applied over the Kairouan plain, the central semiarid region of Tunisia (North Africa). Soil moisture products were first validated on the basis of comparisons with Global Soil Wetness Project, Phase 2 Data, outputs and rainfall events. The soil moisture distribution during the rainy period between October and May is described and is found to be correlated with the vegetation dynamics estimated using the NDVI products. Finally, a semiempirical model is proposed, based on satellite moisture and NDVI products, which allows the NDVI value to be estimated for a period of 1 month during the rainy season as a function of the moisture profile estimations obtained during the previous months. This approach could prove very useful and provide a simple tool for the modeling of vegetation dynamics during rainy seasons in semiarid regions.

Derivation of wild vegetation cover density in semi-arid regions: ERS2/SAR evaluation

In this paper, a simple model is proposed for measuring the vegetation cover over soil surfaces from radar signals acquired in semi-arid regions. In such regions, vegetation is characterized by the presence of clumps which partially cover the soil surface. The proposed model describes the relationship between the percentage of covered surface and the measured radar signal. Model simulations over Tunisian test areas, where ground parameters are controlled, are performed and compared with actual ERS2 radar measurements. A very good agreement is found. The model is then used to derive a map of the vegetation cover density for the whole studied site (in Tunisia). The approach used here is based upon supervised classification with classes defined by inverting the model and taking into account ERS calibration error. Each of the four classes thus defined exhibits a good classification rate, greater than 85%. Finally, two important applications for natural resources management are presented: vegetation monitoring and soil moisture monitoring.

A New Land Surface Hydrology within the Noah-WRF Land-Atmosphere Mesoscale Model Applied to Semiarid Environment: Evaluation over the Dantiandou Kori (Niger)

Land-atmosphere feedbacks, which are particularly important over the Sahel during the West African Monsoon (WAM), partly depend on a large range of processes linked to the land surface hydrology and the vegetation heterogeneities. This study focuses on the evaluation of a new land surface hydrology within the Noah-WRF land-atmosphere-coupled mesoscale model over the Sahel. This new hydrology explicitly takes account for the Dunne runoff using topographic information, the Horton runoff using a Green-Ampt approximation, and land surface heterogeneities. The previous and new versions of Noah-WRF are compared against a unique observation dataset located over the Dantiandou Kori (Niger). This dataset includes dense rain gauge network, surfaces temperatures estimated from MSG/SEVIRI data, surface soil moisture mapping based on ASAR/ENVISAT C-band radar data and in situ observations of surface atmospheric and land surface energy budget variables. Generally, the WAM is reasonably reproduced by Noah-WRF even if some limitations appear throughout the comparison between simulations and observations. An appreciable improvement of the model results is also found when the new hydrology is used. This fact seems to emphasize the relative importance of the representation of the land surface hydrological processes on the WAM simulated by Noah-WRF over the Sahel.

Use of ERS/SAR measurements for soil geometric and aerodynamic roughness estimation in semi-arid and arid areas

This paper discusses the potential of radar signal to characterise the bare surface roughness in arid or semi-arid regions. The used microwave sensor is the SAR of ERS. Ground truth measurements were acquired over different arid sites in the South of Tunisia. An empirical approach is proposed to derive the surface roughness from SAR measurements. The relationships with two different kinds of roughness have been studied: the geometric roughness, which is characterised by a rather new parameter called Zs, and the classical aerodynamic roughness Z/sub 0/.

Retrieval of elevation by radarclinometry in arid or semi‐arid regions

The aim of this work is to extend radarclinometry technique to regions where the two current assumptions, (i) Lambertian backscattering and (ii) homogeneous areas, are not possible. For this, we replace the traditional Lambertian model by a backscattering diagram provided by the integral equation model (IEM). Then, to take into account the roughness heterogeneity of the region, we introduce a classification process. In the global algorithm for DEM (digital elevation model), the first iteration classification is determined according to minimum distance, and then the derived altitude is also used to constrain classification. The process is iterative. Validation is performed on four sites in Israel and Tunisia, which represent different cases of arid or semi‐arid regions. Both qualitative and quantitative results are satisfactory. Limits of the method are stressed by the sand dune case.

Surface soil moisture estimation using active microwave ERS wind scatterometer and SAR data

This paper presents an original methodology to retrieve surface (< 5 cm) soil moisture over low vegetated regions using the two active microwave instruments of ERS satellite. The developed algorithm takes advantage of the multi-angular configuration and high temporal resolution of the Wind Scatterometer (WSC) combined with the SAR high spatial resolution. High correlations (R/sup 2/ greater than 0.8) are observed for three studied watersheds in France with an rms error smaller than 4% between real and retrieved moistures.

Estimation of spatial and temporal evolution of vegetation and surface moisture from ERS2 radar in a semi-arid region

In this paper, a simple model is proposed for retrieving the vegetation cover over soil surfaces from radar signals acquired in semi-arid regions. In Tunisia, vegetation is characterized by the presence of clumps which partially cover the soil surface. The proposed model describes the relationship between the percentage of covered surface and the measured radar signal. Model simulations compared with actual ERS2 radar data show a very good agreement. A map of the whole studied site (in Tunisia) is then derived combining classification approach and previous modeling.